| Claudio | Christophe | Bengt | Per |
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| Marcin | Mikael | Fredrik | Jason |
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Participants expect the course to deepen their understanding of neural nets and deep learning techniques, provide hands-on experience, and teach them how to apply these techniques to their specific research fields. Some participants hope to learn how to recognize when and how to use these techniques to improve their data analysis, while others hope to develop their own versions of neural net tools.
(Summary of your answers, generated by ChatGPT)
https://uppsala.instructure.com/courses/75565
import sys, os
sys.path.append(os.path.abspath(os.path.join('..', 'common_assets')))
import numpy as np
import matplotlib.pyplot as plt
from draw_neural_net import draw_neural_net
layer_sizes = [ 4, 3, 2, 3, 4 ]
weights = [
np.array(
[
[ "w_{1,1}", "w_{1,2}", "w_{1,3}"],
[ "w_{2,1}", "w_{2,2}", "w_{2,3}"],
[ "w_{3,1}", "w_{3,2}", "w_{3,3}"],
[ "w_{4,1}", "w_{4,2}", "w_{4,3}"]
]
),
np.array(
[
[ "w_{1,1}", "w_{1,2}"],
[ "w_{2,1}", "w_{2,2}"],
[ "w_{3,1}", "w_{3,2}"]
]
),
# np.array(
# [
# [ "w_{1,1}", "w_{1,2}"],
# [ "w_{2,1}", "w_{2,2}"],
# ]
# ),
np.array(
[
[ "w_{1,1}", "w_{1,2}", "w_{1,3}"],
[ "w_{2,1}", "w_{2,2}", "w_{2,3}"]
]
),
np.array(
[
[ "w_{1,1}", "w_{1,2}", "w_{1,3}","w_{1,4}"],
[ "w_{2,1}", "w_{2,2}", "w_{2,3}","w_{2,4}"],
[ "w_{3,1}", "w_{3,2}", "w_{3,3}","w_{3,4}"]
]
)
]
biases = [
np.array(
["b_1", "b_2","b_3"]
),
np.array(
["b_1", "b_2"]
),
np.array(
["b_1", "b_2", "b_3"]
),
np.array(
["b_1", "b_2", "b_3", "b_4"]
),
]
figAE = plt.figure(figsize=(8,5))
ax = figAE.gca()
ax.axis('off')
draw_neural_net(ax, layerSizes=layer_sizes) #, weights= weights, biases=biases)
(taken from https://medium.com/ai-in-plain-english/artificial-intelligence-vs-machine-learning-vs-deep-learning-whats-the-difference-dccce18efe7f)
(taken from https://www.sciencedirect.com/topics/neuroscience/perceptron)
(taken from https://news.cornell.edu/stories/2019/09/professors-perceptron-paved-way-ai-60-years-too-soon)
(taken from https://www.kdnuggets.com/2019/01/backpropagation-algorithm-demystified.html)
(taken from https://predictioncenter.org)
(taken from https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53)
Written and spoke language recognition
(taken from Mouret, Jean-Baptiste & Doncieux, Stéphane. (2008). Evolutionary Intelligence
Risk predictions
(taken from https://spectrum.ieee.org/computing/embedded-systems/bringing-big-neural-networks-to-selfdriving-cars-smartphones-and-drones)
Forecasting, weather, business
(taken from https://www.gjesm.net/article_23079.html)
Face recognition and generation
(taken from https://medium.com/syncedreview/gan-2-0-nvidias-hyperrealistic-face-generator-e3439d33ebaf)
Protein structure prediction
(taken from https://www.theverge.com/2020/12/1/21754310/deepmind-alphafold-ai-protein-folding-casp-competition)
Bioimaging diagnostics
(taken from https://peerj.com/articles/6201/)